Supplementary Results

Total Page:16

File Type:pdf, Size:1020Kb

Supplementary Results SUPPLEMENTARY RESULTS Supplementary Table 1. Complete listing of transcription factor genes determined to bind differentially in modern human and Neanderthal promoteromes. TF name Wilcoxon stat Wilcoxon pval HGNC ID UniProt ID Description Family LHX3 442284.5 4.83E-38 6595 Q9UBR4 LIM/homeobox protein Lhx3 homeodomain transcription factor(PC00119) PROP1 271411.5 3.18E-35 9455 O75360 Homeobox protein prophet of Pit-1 FOXC1 450055.5 3.71E-33 3800 Q12948 Forkhead box protein C1 winged helix/forkhead transcription factor(PC00246) POU4F3 865739 1.14E-26 9220 Q15319 POU domain, class 4, transcription factor 3 POU4F1 638453.5 2.33E-26 9218 Q01851 POU domain, class 4, transcription factor 1 HOXD8 908880 6.68E-23 5139 P13378 Homeobox protein Hox-D8 FOXB1 510034 9.09E-23 3799 Q99853 Forkhead box protein B1 winged helix/forkhead transcription factor(PC00246) FOXQ1 134647.5 1.03E-21 20951 Q9C009 Forkhead box protein Q1 winged helix/forkhead transcription factor(PC00246) POU4F2 1135242 1.39E-19 9219 Q12837 POU domain, class 4, transcription factor 2 PHOX2A 452784.5 1.45E-19 691 O14813 Paired mesoderm homeobox protein 2A homeodomain transcription factor(PC00119) ARID3B 256994 1.68E-19 14350 Q8IVW6 AT-rich interactive domain-containing protein 3B DNA-binding transcription factor(PC00218) ONECUT2 313241.5 2.12E-19 8139 O95948 One cut domain family member 2 homeodomain transcription factor(PC00119) PHOX2B 510851 9.20E-18 9143 Q99453 Paired mesoderm homeobox protein 2B homeodomain transcription factor(PC00119) PAX7 539656 1.00E-17 8621 P23759 Paired box protein Pax-7 POU3F3 631771 4.21E-17 9216 P20264 POU domain, class 3, transcription factor 3 MAFF 23605 8.03E-17 6780 Q9ULX9 Transcription factor MafF basic leucine zipper transcription factor(PC00056) POU5F1 (POU5F1::SOX2) 43260 8.11E-16 9221 Q01860 POU domain, class 5, transcription factor 1 SOX2 (POU5F1::SOX2) 43260 8.11E-16 11195 P48431 Transcription factor SOX-2 HMG box transcription factor(PC00024) POU2F2 898740 2.16E-15 9213 P09086 POU domain, class 2, transcription factor 2 POU3F1 608911.5 7.17E-15 9214 Q03052 POU domain, class 3, transcription factor 1 ONECUT3 273456 1.17E-14 13399 O60422 One cut domain family member 3 homeodomain transcription factor(PC00119) POU3F2 689576.5 1.92E-14 9215 P20265 POU domain, class 3, transcription factor 2 POU2F1 732151 2.01E-14 9212 P14859 POU domain, class 2, transcription factor 1 FOXC2 296082 1.08E-13 3801 Q99958 Forkhead box protein C2 winged helix/forkhead transcription factor(PC00246) ARID5A 778558 2.08E-13 17361 Q03989 AT-rich interactive domain-containing protein 5A transcription cofactor(PC00217) ONECUT1 131454.5 5.31E-12 8138 Q9UBC0 Hepatocyte nuclear factor 6 homeodomain transcription factor(PC00119) ARID3A 47976.5 1.51E-11 3031 Q99856 AT-rich interactive domain-containing protein 3A DNA-binding transcription factor(PC00218) HOXC10 243032.5 2.52E-11 5122 Q9NYD6 Homeobox protein Hox-C10 CUX1 586 4.24E-11 2557 P39880 Homeobox protein cut-like 1 homeodomain transcription factor(PC00119) POU1F1 1122196 4.30E-11 9210 P28069 Pituitary-specific positive transcription factor 1 POU5F1B 488691 2.07E-09 9223 Q06416 Putative POU domain, class 5, transcription factor 1B MEF2D 437577.5 2.26E-09 6997 Q14814 Myocyte-specific enhancer factor 2D MADS box transcription factor(PC00250) MEF2A 441549 2.41E-09 6993 Q02078 Myocyte-specific enhancer factor 2A MADS box transcription factor(PC00250) NKX2-5 773 7.84E-09 2488 P52952 Homeobox protein Nkx-2.5 homeodomain transcription factor(PC00119) TBX19 4898.5 9.31E-09 11596 O60806 T-box transcription factor TBX19 Rel homology transcription factor(PC00252) LIN54 10569.5 1.05E-08 25397 Q6MZP7 Protein lin-54 homolog PAX3 224681 1.12E-08 8617 P23760 Paired box protein Pax-3 IRF1 4225 1.31E-08 6116 P10914 Interferon regulatory factor 1 winged helix/forkhead transcription factor(PC00246) MECOM 15265.5 3.32E-08 3498 Q03112 Histone-lysine N-methyltransferase MECOM C2H2 zinc finger transcription factor(PC00248) HOXD9 86178.5 3.70E-08 5140 P28356 Homeobox protein Hox-D9 SIX3 3398 5.20E-08 10889 O95343 Homeobox protein SIX3 homeodomain transcription factor(PC00119) MAFK 7199.5 6.53E-08 6782 O60675 Transcription factor MafK basic leucine zipper transcription factor(PC00056) HOXD11 279435.5 1.28E-07 5134 P31277 Homeobox protein Hox-D11 CDX1 172275 2.60E-07 1805 P47902 Homeobox protein CDX-1 homeodomain transcription factor(PC00119) POU3F4 533271 2.67E-07 9217 P49335 POU domain, class 3, transcription factor 4 POU6F1 77984 5.00E-07 9224 Q14863 POU domain, class 6, transcription factor 1 HOXA13 61766 6.26E-07 5102 P31271 Homeobox protein Hox-A13 CEBPA 31 1.23E-06 1833 P49715 CCAAT/enhancer-binding protein alpha basic leucine zipper transcription factor(PC00056) BARHL2 15912 1.48E-06 954 Q9NY43 BarH-like 2 homeobox protein homeodomain transcription factor(PC00119) UNCX -399 3.70E-06 33194 A6NJT0 Homeobox protein unc-4 homolog LMX1B 146337.5 3.95E-06 6654 O60663 LIM homeobox transcription factor 1-beta homeodomain transcription factor(PC00119) MEF2B 542518 4.27E-06 6995 Q02080 Myocyte-specific enhancer factor 2B MADS box transcription factor(PC00250) OTX2 97 5.41E-06 8522 P32243 Homeobox protein OTX2 homeodomain transcription factor(PC00119) HOXD13 90020 1.05E-05 5136 P35453 Homeobox protein Hox-D13 HOXA10 172880 2.30E-05 5100 P31260 Homeobox protein Hox-A10 POU2F3 535473 3.18E-05 19864 Q9UKI9 POU domain, class 2, transcription factor 3 HNF1A 434983 5.38E-05 11621 P20823 Hepatocyte nuclear factor 1-alpha DNA-binding transcription factor(PC00218) HESX1 -684 5.45E-05 4877 Q9UBX0 Homeobox expressed in ES cells 1 HNF1B 294035.5 6.01E-05 11630 P35680 Hepatocyte nuclear factor 1-beta DNA-binding transcription factor(PC00218) MAFG 1054.5 6.43E-05 6781 O15525 Transcription factor MafG basic leucine zipper transcription factor(PC00056) IRF2 557 9.28E-05 6117 P14316 Interferon regulatory factor 2 winged helix/forkhead transcription factor(PC00246) ZNF384 67.5 0.0001402 11955 Q8TF68 Zinc finger protein 384 C2H2 zinc finger transcription factor(PC00248) FOXA2 3489 0.0001624 5022 Q9Y261 Hepatocyte nuclear factor 3-beta winged helix/forkhead transcription factor(PC00246) HMX3 50292 0.0003858 5019 A6NHT5 Homeobox protein HMX3 HMX2 46211 0.0005692 5018 A2RU54 Homeobox protein HMX2 NKX6-2 6468 0.0005991 19321 Q9C056 Homeobox protein Nkx-6.2 homeodomain transcription factor(PC00119) IRF9 1844 0.0007433 6131 Q00978 Interferon regulatory factor 9 winged helix/forkhead transcription factor(PC00246) NR1H3 (NR1H3::RXRA) 685 0.001769 7966 Q13133 Oxysterols receptor LXR-alpha C4 zinc finger nuclear receptor(PC00169) RXRA (NR1H3::RXRA) 685 0.001769 10477 P19793 Retinoic acid receptor RXR-alpha C4 zinc finger nuclear receptor(PC00169) OLIG2 3491.5 0.0018305 9398 Q13516 Oligodendrocyte transcription factor 2 basic helix-loop-helix transcription factor(PC00055) STAT6 -598 0.001918 11368 P42226 Signal transducer and activator of transcription 6 DNA-binding transcription factor(PC00218) MEF2C 351 0.0020757 6996 Q06413 Myocyte-specific enhancer factor 2C MADS box transcription factor(PC00250) FOXF2 29 0.0023199 3810 Q12947 Forkhead box protein F2 VENTX 14790.5 0.0036602 13639 O95231 Homeobox protein VENTX homeodomain transcription factor(PC00119) NR2E1 1400.5 0.0040203 7973 Q9Y466 Nuclear receptor subfamily 2 group E member 1 C4 zinc finger nuclear receptor(PC00169) DUXA 1735 0.0043964 32179 A6NLW8 Double homeobox protein A PITX1 378 0.0045946 9004 P78337 Pituitary homeobox 1 ISL2 132 0.0046249 18524 Q96A47 Insulin gene enhancer protein ISL-2 homeodomain transcription factor(PC00119) FOXD3 102497 0.0046328 3804 Q9UJU5 Forkhead box protein D3 winged helix/forkhead transcription factor(PC00246) TCF7L2 1157.5 0.0051148 11641 Q9NQB0 Transcription factor 7-like 2 EVX1 248 0.0064663 3506 P49640 Homeobox even-skipped homolog protein 1 FOXH1 1345 0.0071219 3814 O75593 Forkhead box protein H1 OTX1 1128.5 0.008041 8521 P32242 Homeobox protein OTX1 homeodomain transcription factor(PC00119) PBX3 -8 0.0082676 8634 P40426 Pre-B-cell leukemia transcription factor 3 homeodomain transcription factor(PC00119) CEBPB 2082 0.0082978 1834 P17676 CCAAT/enhancer-binding protein beta basic leucine zipper transcription factor(PC00056) FOS (FOS::JUN(VAR.2)) -217.5 0.0095211 3796 P01100 Proto-oncogene c-Fos basic leucine zipper transcription factor(PC00056) JUN (FOS::JUN(VAR.2)) -217.5 0.0095211 6204 P05412 Transcription factor AP-1 basic leucine zipper transcription factor(PC00056) HOXB5 7268 0.0096238 5116 P09067 Homeobox protein Hox-B5 homeodomain transcription factor(PC00119) Supplementary Figure 1. Aggregate expression of DB TFs in 100 tissues. FANTOM5 RNA-Seq data was extracted as TPM values and the 100 tissues with highest aggregate expression of the differentially binding TF genes were selected for clustering (Figure 1), resulting in order of tissues shown here. Supplementary Table 2. Ontological terms (Biological Process and Disease) associated with top 100 marker genes of cortical brain cells clusters expressing DB TFs. Supplementary Figure 2. ROC Analysis model performance in the identification of experimentally verified functional TFBSs – random Ensembl transcripts. ROC analysis was performed using experimentally verified functional TFBSs as annotated in the ORegAnno/Pleides/ABS datasets as true positives, where true negatives were random locations in other Ensembl transcripts at the same distance from the TSS as the associated true positive. All ROC curve analyses were performed on TFs which had at least 10 true positives and 50 true negatives per true positive were used for each analysis. Each true positive/negative segment analyzed was 50 nucleotides long, and the highest TFBS score for the relevant dataset(s) was used for each true positive/negative. (A) Barplot of the frequency of experimental data type in the top 20 performing TFBSFootprinter models. (B) Boxplot of ROC scores for TFBSFootprinter and DeepBind for 14 TFs (left). ROC scores were also calculated based on using individual experimental metrics to show how well each contributes to accuracy of the combined model.
Recommended publications
  • 1 Evidence for Gliadin Antibodies As Causative Agents in Schizophrenia
    1 Evidence for gliadin antibodies as causative agents in schizophrenia. C.J.Carter PolygenicPathways, 20 Upper Maze Hill, Saint-Leonard’s on Sea, East Sussex, TN37 0LG [email protected] Tel: 0044 (0)1424 422201 I have no fax Abstract Antibodies to gliadin, a component of gluten, have frequently been reported in schizophrenia patients, and in some cases remission has been noted following the instigation of a gluten free diet. Gliadin is a highly immunogenic protein, and B cell epitopes along its entire immunogenic length are homologous to the products of numerous proteins relevant to schizophrenia (p = 0.012 to 3e-25). These include members of the DISC1 interactome, of glutamate, dopamine and neuregulin signalling networks, and of pathways involved in plasticity, dendritic growth or myelination. Antibodies to gliadin are likely to cross react with these key proteins, as has already been observed with synapsin 1 and calreticulin. Gliadin may thus be a causative agent in schizophrenia, under certain genetic and immunological conditions, producing its effects via antibody mediated knockdown of multiple proteins relevant to the disease process. Because of such homology, an autoimmune response may be sustained by the human antigens that resemble gliadin itself, a scenario supported by many reports of immune activation both in the brain and in lymphocytes in schizophrenia. Gluten free diets and removal of such antibodies may be of therapeutic benefit in certain cases of schizophrenia. 2 Introduction A number of studies from China, Norway, and the USA have reported the presence of gliadin antibodies in schizophrenia 1-5. Gliadin is a component of gluten, intolerance to which is implicated in coeliac disease 6.
    [Show full text]
  • Down-Regulation of Stem Cell Genes, Including Those in a 200-Kb Gene Cluster at 12P13.31, Is Associated with in Vivo Differentiation of Human Male Germ Cell Tumors
    Research Article Down-Regulation of Stem Cell Genes, Including Those in a 200-kb Gene Cluster at 12p13.31, Is Associated with In vivo Differentiation of Human Male Germ Cell Tumors James E. Korkola,1 Jane Houldsworth,1,2 Rajendrakumar S.V. Chadalavada,1 Adam B. Olshen,3 Debbie Dobrzynski,2 Victor E. Reuter,4 George J. Bosl,2 and R.S.K. Chaganti1,2 1Cell Biology Program and Departments of 2Medicine, 3Epidemiology and Biostatistics, and 4Pathology, Memorial Sloan-Kettering Cancer Center, New York, New York Abstract on the degree and type of differentiation (i.e., seminomas, which Adult male germ cell tumors (GCTs) comprise distinct groups: resemble undifferentiated primitive germ cells, and nonseminomas, seminomas and nonseminomas, which include pluripotent which show varying degrees of embryonic and extraembryonic embryonal carcinomas as well as other histologic subtypes patterns of differentiation; refs. 2, 3). Nonseminomatous GCTs are exhibiting various stages of differentiation. Almost all GCTs further subdivided into embryonal carcinomas, which show early show 12p gain, but the target genes have not been clearly zygotic or embryonal-like differentiation, yolk sac tumors and defined. To identify 12p target genes, we examined Affymetrix choriocarcinomas, which exhibit extraembryonal forms of differ- (Santa Clara, CA) U133A+B microarray (f83% coverage of 12p entiation, and teratomas, which show somatic differentiation along genes) expression profiles of 17 seminomas, 84 nonseminoma multiple lineages (3). Both seminomas and embryonal carcinoma GCTs, and 5 normal testis samples. Seventy-three genes on 12p are known to express stem cell markers, such as POU5F1 (4) and were significantly overexpressed, including GLUT3 and REA NANOG (5).
    [Show full text]
  • HNF6 Antibody (R31338)
    HNF6 Antibody (R31338) Catalog No. Formulation Size R31338 0.5mg/ml if reconstituted with 0.2ml sterile DI water 100 ug Bulk quote request Availability 1-3 business days Species Reactivity Human, Mouse, Rat Format Antigen affinity purified Clonality Polyclonal (rabbit origin) Isotype Rabbit IgG Purity Antigen affinity Buffer Lyophilized from 1X PBS with 2.5% BSA and 0.025% sodium azide/thimerosal UniProt Q9UBC0 Applications Western blot : 0.5-1ug/ml IHC (FFPE) : 0.5-1ug/ml IHC (Frozen) : 0.5-1ug/ml Immunocytochemistry : 0.5-1ug/ml Limitations This HNF6 antibody is available for research use only. Western blot testing of HNF6 antibody and Lane 1: rat liver; 2: mouse liver; 3: human HeLa cell lysate. Expected/observed size ~51KD IHC-P: HNF6 antibody testing of human liver cancer tissue ICC testing of HNF6 antibody and HCT116 cells IHC-F testing of rat liver tissue IHC-P testing of rat liver tissue Description One cut homeobox 1 (ONECUT1), also called Hepatocyte nuclear factor 6 (HNF6) is found strong expression in liver and lower expression in testis and skin. The gene encodes a member of the Cut homeobox family of transcription factors. Expression of the encoded protein is enriched in the liver, where it stimulates transcription of liver-expressed genes, and antagonizes glucocorticoid-stimulated gene transcription. This gene may influence a variety of cellular processes including glucose metabolism, cell cycle regulation, and it may also be associated with cancer. Application Notes The stated application concentrations are suggested starting amounts. Titration of the HNF6 antibody may be required due to differences in protocols and secondary/substrate sensitivity.
    [Show full text]
  • Derivation of Stable Microarray Cancer-Differentiating Signatures Using Consensus Scoring of Multiple Random Sampling and Gene-Ranking Consistency Evaluation
    Research Article Derivation of Stable Microarray Cancer-Differentiating Signatures Using Consensus Scoring of Multiple Random Sampling and Gene-Ranking Consistency Evaluation Zhi Qun Tang,1,2 Lian Yi Han,1,2 Hong Huang Lin,1,2 Juan Cui,1,2 Jia Jia,1,2 Boon Chuan Low,2,3 Bao Wen Li,2,4 and Yu Zong Chen1,2 1Bioinformatics and Drug Design Group, Department of Pharmacy; 2Center for Computational Science and Engineering; and Departments of 3Biological Sciences and 4Physics, National University of Singapore, Singapore, Singapore Abstract sampling methods. Only 1 to 5 of the 4 to 60 selected predictor Microarrays have been explored for deriving molecular genes in each of these sets are present in more than half of the signatures to determine disease outcomes, mechanisms, other nine sets (Table 1), and 2 to 20 of the predictor genes in each targets, and treatment strategies. Although exhibiting good set are cancer related (Table 2). Despite the use of sophisticated predictive performance, some derived signatures are unstable class differentiation and signature selection methods, the selected due to noises arising from measurement variability and signatures show few overlapping predictor genes, as in the case of biological differences. Improvements in measurement, anno- other microarray data sets including non–Hodgkin lymphoma, tation, and signature selection methods have been proposed. acute lymphocytic leukemia, breast cancer, lung adenocarcinoma, We explored a new signature selection method that incorpo- medulloblastoma, hepatocellular carcinoma, and acute myeloid rates consensus scoring of multiple random sampling and leukemia (9, 15). multistep evaluation of gene-ranking consistency for maxi- Although these signatures display high cancer differentiation mally avoiding erroneous elimination of predictor genes.
    [Show full text]
  • EXTENDED MATERIALS and METHODS Animal Experimentation. All Experiments Were Performed in Agreement with the Swiss Law on Animal
    EXTENDED MATERIALS AND METHODS Animal experimentation. All experiments were performed in agreement with the Swiss law on animal protection (LPA), under license No GE 81/14 (to DD). In situ hybridization. Whole mount in situ hybridizations were performed as described in (Woltering et al., 2014). Probes for the Hoxa11, Hoxa13, Hoxd8, Hoxd10, Hoxd12, Hoxd13 and Evx2 genes were synthetized and purified as previously reported (Herault et al., 1996; Woltering et al., 2014). Plasmids encoding the cDNAs of the Prrx2 and Dbx2 genes were purchased from Addgene and probes were synthetized as previously reported (Pierani et al., 1999; Stelnicki et al., 1998). Right or left forelimbs were dissected from stained embryos and photographied dorsally with a Leica MZ16 stereomicroscope. Pictures from left forelimbs are displayed inverted. RNA extraction. Total RNA was extracted from individual pairs of either wild type or double Hox13-/- mutant proximal and distal forelimb buds, using the RNeasy Micro Kit (Qiagen) following manufacturer instructions. A total of 100ng of pure total RNA was amplified following standard Illumina procedure for polyA-selected RNA. RNA-seq data generation. RNA sequencing (RNA-seq) libraries were prepared with the Illumina TruSeq Stranded mRNA protocol and sequenced on a HiSeq 2500 machine, as single-end, 100 base pairs (bp) reads. The preparation of libraries and sequencing were performed by the genomic platform of the University of Geneva. RNA-seq data analysis. A mutant version of the genome, encoding the Hoxd13/LacZ and the Hoxa13/Neo+ alleles (Fromental-Ramain et al., 1996; Kondo et al., 1998), was assembled and annotated and used as reference genome to map the Hoxa13-/- and Hoxd13-/- RNA-seq data.
    [Show full text]
  • Olig2 and Ngn2 Function in Opposition to Modulate Gene Expression in Motor Neuron Progenitor Cells
    Downloaded from genesdev.cshlp.org on September 29, 2021 - Published by Cold Spring Harbor Laboratory Press Olig2 and Ngn2 function in opposition to modulate gene expression in motor neuron progenitor cells Soo-Kyung Lee,1 Bora Lee,1 Esmeralda C. Ruiz, and Samuel L. Pfaff2 Gene Expression Laboratory, The Salk Institute for Biological Studies, La Jolla, California 92037, USA Spinal motor neurons and oligodendrocytes are generated sequentially from a common pool of progenitors termed pMN cells. Olig2 is a bHLH-class transcription factor in pMN cells, but it has remained unclear how its transcriptional activity is modulated to first produce motor neurons and then oligodendrocytes. Previous studies have shown that Olig2 primes pMN cells to become motor neurons by triggering the expression of Ngn2 and Lhx3. Here we show that Olig2 also antagonizes the premature expression of post-mitotic motor neuron genes in pMN cells. This blockade is counteracted by Ngn2, which accumulates heterogeneously in pMN cells, thereby releasing a subset of the progenitors to differentiate and activate expression of post-mitotic motor neuron genes. The antagonistic relationship between Ngn2 and Olig2 is mediated by protein interactions that squelch activity as well as competition for shared DNA-binding sites. Our data support a model in which the Olig2/Ngn2 ratio in progenitor cells serves as a gate for timing proper gene expression during the development of pMN cells: Olig2high maintains the pMN state, thereby holding cells in reserve for oligodendrocyte generation, whereas Ngn2high favors the conversion of pMN cells into post-mitotic motor neurons. [Keywords: Motor neuron; oligodendrocyte; development; basic helix–loop–helix (bHLH); neurogenin (Ngn); Olig] Supplemental material is available at http://www.genesdev.org.
    [Show full text]
  • A Computational Approach for Defining a Signature of Β-Cell Golgi Stress in Diabetes Mellitus
    Page 1 of 781 Diabetes A Computational Approach for Defining a Signature of β-Cell Golgi Stress in Diabetes Mellitus Robert N. Bone1,6,7, Olufunmilola Oyebamiji2, Sayali Talware2, Sharmila Selvaraj2, Preethi Krishnan3,6, Farooq Syed1,6,7, Huanmei Wu2, Carmella Evans-Molina 1,3,4,5,6,7,8* Departments of 1Pediatrics, 3Medicine, 4Anatomy, Cell Biology & Physiology, 5Biochemistry & Molecular Biology, the 6Center for Diabetes & Metabolic Diseases, and the 7Herman B. Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN 46202; 2Department of BioHealth Informatics, Indiana University-Purdue University Indianapolis, Indianapolis, IN, 46202; 8Roudebush VA Medical Center, Indianapolis, IN 46202. *Corresponding Author(s): Carmella Evans-Molina, MD, PhD ([email protected]) Indiana University School of Medicine, 635 Barnhill Drive, MS 2031A, Indianapolis, IN 46202, Telephone: (317) 274-4145, Fax (317) 274-4107 Running Title: Golgi Stress Response in Diabetes Word Count: 4358 Number of Figures: 6 Keywords: Golgi apparatus stress, Islets, β cell, Type 1 diabetes, Type 2 diabetes 1 Diabetes Publish Ahead of Print, published online August 20, 2020 Diabetes Page 2 of 781 ABSTRACT The Golgi apparatus (GA) is an important site of insulin processing and granule maturation, but whether GA organelle dysfunction and GA stress are present in the diabetic β-cell has not been tested. We utilized an informatics-based approach to develop a transcriptional signature of β-cell GA stress using existing RNA sequencing and microarray datasets generated using human islets from donors with diabetes and islets where type 1(T1D) and type 2 diabetes (T2D) had been modeled ex vivo. To narrow our results to GA-specific genes, we applied a filter set of 1,030 genes accepted as GA associated.
    [Show full text]
  • Pdf Sub-Classification of Patients with a Molecular Alteration Provides Better Response [57]
    Theranostics 2021, Vol. 11, Issue 12 5759 Ivyspring International Publisher Theranostics 2021; 11(12): 5759-5777. doi: 10.7150/thno.57659 Research Paper Homeobox B5 promotes metastasis and poor prognosis in Hepatocellular Carcinoma, via FGFR4 and CXCL1 upregulation Qin He1, Wenjie Huang2, Danfei Liu1, Tongyue Zhang1, Yijun Wang1, Xiaoyu Ji1, Meng Xie1, Mengyu Sun1, Dean Tian1, Mei Liu1, Limin Xia1 1. Department of Gastroenterology, Institute of Liver and Gastrointestinal Diseases, Hubei Key Laboratory of Hepato-Pancreato-Biliary Diseases, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China. 2. Hubei Key Laboratory of Hepato-Pancreato-Biliary Diseases; Hepatic Surgery Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology; Clinical Medicine Research Center for Hepatic Surgery of Hubei Province; Key Laboratory of Organ Transplantation, Ministry of Education and Ministry of Public Health, Wuhan, Hubei, 430030, China. Corresponding author: Dr. Limin Xia, Department of Gastroenterology, Institute of Liver and Gastrointestinal Diseases, Hubei Key Laboratory of Hepato-Pancreato-Biliary Diseases, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China; Phone: 86 27 6937 8507; Fax: 86 27 8366 2832; E-mail: [email protected]. © The author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/). See http://ivyspring.com/terms for full terms and conditions. Received: 2020.12.29; Accepted: 2021.03.17; Published: 2021.03.31 Abstract Background: Since metastasis remains the main reason for HCC-associated death, a better understanding of molecular mechanism underlying HCC metastasis is urgently needed.
    [Show full text]
  • Figure S1. Representative Report Generated by the Ion Torrent System Server for Each of the KCC71 Panel Analysis and Pcafusion Analysis
    Figure S1. Representative report generated by the Ion Torrent system server for each of the KCC71 panel analysis and PCaFusion analysis. (A) Details of the run summary report followed by the alignment summary report for the KCC71 panel analysis sequencing. (B) Details of the run summary report for the PCaFusion panel analysis. A Figure S1. Continued. Representative report generated by the Ion Torrent system server for each of the KCC71 panel analysis and PCaFusion analysis. (A) Details of the run summary report followed by the alignment summary report for the KCC71 panel analysis sequencing. (B) Details of the run summary report for the PCaFusion panel analysis. B Figure S2. Comparative analysis of the variant frequency found by the KCC71 panel and calculated from publicly available cBioPortal datasets. For each of the 71 genes in the KCC71 panel, the frequency of variants was calculated as the variant number found in the examined cases. Datasets marked with different colors and sample numbers of prostate cancer are presented in the upper right. *Significantly high in the present study. Figure S3. Seven subnetworks extracted from each of seven public prostate cancer gene networks in TCNG (Table SVI). Blue dots represent genes that include initial seed genes (parent nodes), and parent‑child and child‑grandchild genes in the network. Graphical representation of node‑to‑node associations and subnetwork structures that differed among and were unique to each of the seven subnetworks. TCNG, The Cancer Network Galaxy. Figure S4. REVIGO tree map showing the predicted biological processes of prostate cancer in the Japanese. Each rectangle represents a biological function in terms of a Gene Ontology (GO) term, with the size adjusted to represent the P‑value of the GO term in the underlying GO term database.
    [Show full text]
  • Supplemental Materials ZNF281 Enhances Cardiac Reprogramming
    Supplemental Materials ZNF281 enhances cardiac reprogramming by modulating cardiac and inflammatory gene expression Huanyu Zhou, Maria Gabriela Morales, Hisayuki Hashimoto, Matthew E. Dickson, Kunhua Song, Wenduo Ye, Min S. Kim, Hanspeter Niederstrasser, Zhaoning Wang, Beibei Chen, Bruce A. Posner, Rhonda Bassel-Duby and Eric N. Olson Supplemental Table 1; related to Figure 1. Supplemental Table 2; related to Figure 1. Supplemental Table 3; related to the “quantitative mRNA measurement” in Materials and Methods section. Supplemental Table 4; related to the “ChIP-seq, gene ontology and pathway analysis” and “RNA-seq” and gene ontology analysis” in Materials and Methods section. Supplemental Figure S1; related to Figure 1. Supplemental Figure S2; related to Figure 2. Supplemental Figure S3; related to Figure 3. Supplemental Figure S4; related to Figure 4. Supplemental Figure S5; related to Figure 6. Supplemental Table S1. Genes included in human retroviral ORF cDNA library. Gene Gene Gene Gene Gene Gene Gene Gene Symbol Symbol Symbol Symbol Symbol Symbol Symbol Symbol AATF BMP8A CEBPE CTNNB1 ESR2 GDF3 HOXA5 IL17D ADIPOQ BRPF1 CEBPG CUX1 ESRRA GDF6 HOXA6 IL17F ADNP BRPF3 CERS1 CX3CL1 ETS1 GIN1 HOXA7 IL18 AEBP1 BUD31 CERS2 CXCL10 ETS2 GLIS3 HOXB1 IL19 AFF4 C17ORF77 CERS4 CXCL11 ETV3 GMEB1 HOXB13 IL1A AHR C1QTNF4 CFL2 CXCL12 ETV7 GPBP1 HOXB5 IL1B AIMP1 C21ORF66 CHIA CXCL13 FAM3B GPER HOXB6 IL1F3 ALS2CR8 CBFA2T2 CIR1 CXCL14 FAM3D GPI HOXB7 IL1F5 ALX1 CBFA2T3 CITED1 CXCL16 FASLG GREM1 HOXB9 IL1F6 ARGFX CBFB CITED2 CXCL3 FBLN1 GREM2 HOXC4 IL1F7
    [Show full text]
  • TF Activation Profiling Plate Array II Signosis, Inc
    Signosis, Inc. Innovative Plate Assay Solutions TF Activation Profiling Plate Array II Catalog Number: FA-1002 (For Research Use Only) Introduction Materials Provided with the Kit Transcription factors (TFs) are a group of cellular proteins that play essential roles in regulating gene Component Qty Store at expression. They act as sensors to monitor cellular 96-Well Plates (with 2 RT changes and convert signals into gene expression. aluminum adhesive seal) Often, a specific cellular signal pathway can activate Isolation Columns 2 RT multiple TFs. The expression of a specific gene can Elution Buffer 400µL RT also be under the control of multiple TFs. Thus, TF Plate Hybridization Buffer 20mL RT monitoring the activation of multiple TFs 5X Plate Hybridization Wash 60mL RT simultaneously is critical to understanding the Buffer molecular mechanism of cellular regulation underlying 5X Detection Wash Buffer 60mL RT cell signaling and gene expression. Signosis, Inc.’s TF Blocking Buffer 60mL RT Activation Profiling Plate Array II is used for Filter Wash Buffer 5mL 4°C monitoring 96 different TFs simultaneously from one Filter Binding Buffer 1mL 4°C sample. Substrate A 2mL 4°C Substrate B 2mL 4°C Principle of the assay Streptavidin-HRP Conjugate 40µL 4°C Substrate Dilution Buffer 16mL 4°C Signosis, Inc.’s TF Activation Profiling Plate Array II TF Binding Buffer Mix 60µL -20°C is used for monitoring the activation of multiple TFs TF Probe Mix II 20µL -20°C simultaneously. With this technology a series of biotin-labeled probes are made based on the consensus sequences of TF DNA-binding sites.
    [Show full text]
  • UNIVERSITY of CALIFORNIA, IRVINE Combinatorial Regulation By
    UNIVERSITY OF CALIFORNIA, IRVINE Combinatorial regulation by maternal transcription factors during activation of the endoderm gene regulatory network DISSERTATION submitted in partial satisfaction of the requirements for the degree of DOCTOR OF PHILOSOPHY in Biological Sciences by Kitt D. Paraiso Dissertation Committee: Professor Ken W.Y. Cho, Chair Associate Professor Olivier Cinquin Professor Thomas Schilling 2018 Chapter 4 © 2017 Elsevier Ltd. © 2018 Kitt D. Paraiso DEDICATION To the incredibly intelligent and talented people, who in one way or another, helped complete this thesis. ii TABLE OF CONTENTS Page LIST OF FIGURES vii LIST OF TABLES ix LIST OF ABBREVIATIONS X ACKNOWLEDGEMENTS xi CURRICULUM VITAE xii ABSTRACT OF THE DISSERTATION xiv CHAPTER 1: Maternal transcription factors during early endoderm formation in 1 Xenopus Transcription factors co-regulate in a cell type-specific manner 2 Otx1 is expressed in a variety of cell lineages 4 Maternal otx1 in the endodermal conteXt 5 Establishment of enhancers by maternal transcription factors 9 Uncovering the endodermal gene regulatory network 12 Zygotic genome activation and temporal control of gene eXpression 14 The role of maternal transcription factors in early development 18 References 19 CHAPTER 2: Assembly of maternal transcription factors initiates the emergence 26 of tissue-specific zygotic cis-regulatory regions Introduction 28 Identification of maternal vegetally-localized transcription factors 31 Vegt and OtX1 combinatorially regulate the endodermal 33 transcriptome iii
    [Show full text]